What is an EEAT Audit?

E‑E‑A‑T is not something you “add” to a page. It’s a reviewer concept and a sanity check Google uses so humans can talk about “is this real, is this safe, is this useful?” If you’re trying to optimize for EEAT as if it’s a ranking factor, you’re already in the wrong frame.

Stop chasing “EEAT signals”

Here’s the first thing you need to accept: there is no EEAT scorecard and there are no EEAT tags or technical signals you can sprinkle around to make rankings go up.

• You can’t “enable EEAT” in your CMS.

• You can’t buy a tool that measures it accurately.

• You definitely can’t fix a weak site by dropping the acronym in your copy.

Most “EEAT optimization” services are just rebranded content or CRO work wrapped in buzzwords to sound more Google‑official than they really are.

EEAT is about impression, not props

Google uses E‑E‑A‑T as a way for humans (quality raters, policy reviewers, abuse teams) to express how a site feels:

• Does this look like a real business or a fake one?

• Is this content written from real experience or scraped/fluffed?

• Would a normal person trust this source on health/money/legal topics?

That impression comes from the totality of your presence, not from a single trick:

• Real entity: clear who runs the site, with consistent names, addresses, and profiles.

• Real footprint: people talk about you off your own domain—links, mentions, citations.

• Real depth: content that shows you’ve actually done the thing, not just summarized the first page of Google.

You can’t “declare” any of that into existence with a byline and a stock headshot.

How to ignore bad EEAT advice

When you see an “EEAT guide,” run it through this filter:

• If it sounds like a checklist (“add author schema, add About page, add FAQs”) and ignores links, reputation, or actual expertise, treat it as decoration advice.

• If it claims “Google measures EEAT signals” but never explains real, verifiable mechanisms, it’s storytelling for sales, not reality.

• If it sells an “EEAT audit” that looks like a content rewrite with some boilerplate bios, you’re paying for lipstick on a pig.

Use EEAT as a mental model: “would a human with the power to nuke spam or restore false positives look at this site and consider it real, competent, and safe?” If the answer is yes, congratulations—you don’t have an EEAT problem; you have a classic SEO problem: crawl, index, competition, links, intent, and content quality.

Spend your time fixing those. The more your site behaves like an actually trustworthy entity in the real world, the less you ever need to say the word “EEAT” at all.

The post Guide to Google EEAT Audits appeared first on Primary Position SEO NYC.

SEO competitive intelligence is the difference between “we need more content” and “we know exactly which competitors to steal traffic from, in what order, and how.” On PrimaryPosition.com, this lens turns that into a simple operating system: relevance vs authority, mapped directly against real competitors instead of generic best practices.

What SEO competitive intelligence really means

SEO competitive intelligence is the practice of turning competing sites and SERPs into a structured dataset: which pages win, for which queries, with what intent, and why. It focuses less on “how do we rank” and more on “what specific moves will displace these URLs in this niche, at this stage of our authority.”

Traditional checklists and plugin “scores” confuse loud on‑page signaling with actual win probability; competitive intelligence corrects that by tying every decision to the shape of real, current SERPs. Instead of chasing a 95/100 grade, you are measuring where you can realistically win and where the math is against you.

Topic relevance vs authority grid

Our founders scorecard philosophy—already baked into Primary Position’s content—reduces every SERP to two levers: Relevance and Authority. Competitive intelligence is simply how those two levers get quantified per query and per competitor.

• Relevance:

  • Exact query and intent definition for each target cluster.

  • URL, title, H1, intro, and internal anchor text that clearly commit to that job rather than five half‑targets at once.

  • SERP fit: whether your format (guide, comparison, tool, category) matches what Google is already rewarding.

• Authority:

  • Topic‑level strength of your domain against others in that niche, not global domain metrics.

  • Page‑level support: internal links, external links, and real‑world brand footprint (mentions, reviews, discussion).

  • Behavioral proof over time: pages that actually retain and convert traffic instead of briefly ranking then dying.

For Primary Position, an “SEO competitive intelligence” process is just a scaled version of that same grid applied to the top competing pages across a cluster, with each row telling you where your bottleneck truly sits.

A Primary Position-style SEO CI workflow

A  workflow for competitive intelligence at PrimaryPosition.com is intentionally low‑theatre and high‑signal, usually managed in a spreadsheet or Notion, not just a SaaS dashboard.

1. Map real, search-based competitors

   • Pull the live SERPs for your money queries and cluster who actually appears, ignoring who you merely consider “industry peers.”

   • For each query or cluster, list the top 5–10 URLs that repeatedly show up—those domains become your working competitor set.

2. Capture per‑query competitive snapshots

   • For each cluster, record: target query, user job, top competitor URLs, and the apparent page type that wins.

   • Add quick 0/1/2 scores for competitor relevance (how tightly they hit the job) and authority (how clearly the domain and page are trusted in that topic).

3. Decide on “steal, leapfrog, or sidestep”

   • Steal: where your authority is comparable and competitors’ relevance is average, you can win by focusing a better page and wiring it properly.

   • Leapfrog: where you are weaker today but the topic is core, you plan deliberate authority building (links, relationships, brand) to earn a place in that cluster.

   • Sidestep: where the math is unwinnable right now, you shift into adjacent, easier clusters that still drive the right buyers.

4. Turn it into a living, not static, document

   • We treat SEO as an ongoing conversation with reality, not a one‑time checklist; the competitive scorecard is updated as SERPs shift, not annually.​

   • Each update is less “new report” and more “changed rows”: new threats, new openings, and newly winnable clusters as your authority grows.

How this differs from plugin “competitor analysis”

Most plugin or all‑in‑one tools bolt on a “competitor” tab that lists overlapping keywords and maybe an index of who ranks with you. They do not typically force you to answer the hard questions: “Can we even be relevant here?” and “Do we have enough authority to matter?”

A style system that is deliberately opinionated:

• It assumes that without clear query/intent definition and a focused page, you are not a real competitor at all.

• It assumes that in over‑contested topics, ranking is primarily an authority problem, not a word count or green‑light problem.

By framing SEO competitive intelligence around a simple, human‑readable scorecard, Primary Position makes “what are our competitors doing?” a concrete roadmap decision instead of an anxiety spiral.

The post Getting ahead with SEO Competitive Intelligence appeared first on Primary Position SEO NYC.

PrimaryPosition.com: An AI-First LLM SEO Agency

Most “LLM SEO” pitches are lipstick on the same old checklists. PrimaryPosition.com takes the opposite approach: it treats AI and LLM search as a structural change in how users’ questions are decomposed, retrieved, and answered—then rebuilds SEO strategy around that reality.

From Classic SEO to AI-First Search

Primary Position started life as a search-first agency and grew up in the era of blue links, Panda, Penguin, and HCU-style quality updates. That background matters, because the team isn’t guessing how AI search works in a vacuum. They understand that LLMs sit on top of retrieval systems that still look a lot like Google and Bing, with all the bias and mess that implies.

Instead of selling “AI magic,” they focus on:

• How questions actually get asked in 2026: chat, answer boxes, AI overviews, and voice.

• How those questions get exploded into many machine-generated queries behind the scenes.

• How brands can own those expanded demand surfaces in a measurable way.

The result is an agency that still speaks SEO—but with an AI-native accent.

The Truth About “LLM SEO”

Primary Position’s core stance is simple: there is no separate “LLM ranking algorithm” you can optimize for in isolation. LLMs answer using some mix of:

• Model memory (training data and weights).

• Live retrieval from search engines and APIs.

• Private or product-specific data streams.

You don’t get to control the first bucket. The third belongs to platforms. So the agency focuses where brands actually have leverage: the live retrieval layer and the web footprint those systems can’t ignore.

That means no chasing:

• LLMS.txt “protocols” that promise direct model control but have no adoption.

• Thin rewrites “for AI” that ignore search demand and user intent.

• Rituals around E‑E‑A‑T checklists that LLMs do not literally use as ranking signals.

Instead, they work on the boring, hard things that actually move visibility: better retrieval coverage, stronger entity presence, and content that’s easy to quote.

Query Fan-Out: The New Battleground

A central idea in Primary Position’s AI playbook is “query fan-out.” When a user types one natural-language question into an AI box, the system doesn’t fire one clean keyword at a search index. It explodes that question into a cluster of related queries and evidence requests.

In practice, that means:

• Ranking for one trophy keyword is no longer enough.

• Brands need to show up across the whole cluster of “how to”, “best for”, “X vs Y”, “alternatives to” and problem-shaped queries that the system fans out into.

• LLM visibility problems are often just retrieval coverage problems in disguise.

PrimaryPosition.com builds strategies around these clusters rather than single phrases. That changes everything from how they do keyword research to how they structure content, comparison pages, and support material.

What Primary Position Actually Does as an LLM SEO Agency

Rather than bolting “AI” onto existing services, Primary Position rebuilds the stack with LLM search in mind. Typical work includes:

• Mapping AI demand surfaces

  • Identifying where AI answer boxes, overviews, and chat products are heavily used in a given vertical.

  • Reverse-engineering the likely fan-out queries behind those experiences.

• Designing content for retrieval and quoting

  • Building pages that explicitly answer the kinds of comparison, evaluation, and “what should I use?” questions AI tools need to handle.

  • Structuring content so that key claims and definitions are easy to lift and summarize.

• Expanding entity and brand presence

  • Getting brands mentioned (not just linked) in the places AI systems lean on most: strong blogs, docs, communities, and platforms that reliably rank.

  • Closing gaps where competitors are present in third-party content and you are not.

• Measuring AI / LLM visibility

  • Tracking branded presence in AI answer UIs over time.

  • Correlating changes in classic SEO (rankings, click-through, content launches) with changes in mention/citation rates in AI responses.

The output isn’t “we added FAQ schema.” It’s “your brand now shows up in the kinds of answers your buyers see when they ask AI tools for help.”

The post An AI LLM SEO Agency Approach appeared first on Primary Position SEO NYC.

What are LLMs?

LLMs are not a new search engine. They are a lossy compression layer sitting on top of the same messy, biased, commercial web we have all been optimizing for the last 20 years, plus a bunch of proprietary data you will never see. Their job is not to “rank” your content. Their job is to synthesize patterns from whatever they are fed and return something that looks coherent and useful to a human.

LLMs are not using your SEO playbook

Most of the industry is still treating LLMs like “Google but chattier.” That is a category error. These systems do not have an E‑E‑A‑T knob. They do not reward your perfect H2 hierarchy or your 8th‑grade reading level. They consume token streams. They happily ingest PDFs, code comments, forum rants, transcripts, and mangled HTML and compress all of it into parameters.

What does LLM visibility mean?

For real‑time answers, tools like ChatGPT, Perplexity, or Gemini lean on search systems and APIs that look a lot like Bing/Google under the hood. That means your visibility is still bottlenecked by classic retrieval: if those engines do not surface you, the LLM never even gets the chance to see you in the first place. For “frozen” knowledge (model weights, cached corpora, proprietary datasets), you do not control the crawl, the sampling, or the update cycle at all.

Query fan‑out is the real problem

When you test your brand in Google, you see one query and one set of results. When a user types a similar question into an AI answer box, that is not what actually happens. LLM search fans your prompt out into a cluster of related queries, runs those in the background, and assembles an answer from whatever comes back.

So you think “we rank #1 for our core query, why are we invisible in AI answers?” Because your entire strategy is built around one neat keyword and the LLM is hitting ten different messy, long‑tail, comparison‑shaped queries you never bothered to optimize for. In other words: you are not being punished in LLM land, you are being out‑retrieved by your competitors on the expanded query set.

Reverse engineering the Query Fan Out is the real answer to AI Visibility.

This is why people see their brand dominate Google for its own name, yet vanish when they ask “who are the top tools for X?” or “what are alternatives to Y?” The fan‑out shifts the playing field from “can you rank for one trophy keyword?” to “do you show up across the whole question cluster that emerges when people try to actually solve the problem?”

What LLMs actually pull from

When an LLM answers, three broad data sources are in play:

• Its frozen training data and internal weights

• Whatever live data it can fetch through search or APIs

• Any private or first‑party data connected by the user or platform

You do not get to submit your site into the first bucket. You barely see the second bucket, except indirectly through SERPs and citations. The third bucket belongs to whoever controls the user relationship (the SaaS platform, the enterprise deployment, etc.), not to you.

Where do websites and brands sneak in? Primarily through the same routes that already worked for Google:

• Pages that rank for commercial and informational queries

• Content that is heavily linked, referenced, or embedded in other visible properties

• Entities that are consistently mentioned across different sites and content types

That is why Reddit, YouTube, Wikipedia, vendor docs, and big SaaS blogs suddenly feel omnipresent in AI responses. They already owned a huge share of the query fan‑out. The LLM did not “decide” they are authoritative. It inherited that bias from the retrieval stack.

Idiotic myths that refuse to die

Because this is SEO, the myths arrived before the measurements. A few favorites:

• “LLMs prefer fresh content.” Models trained on snapshots of the web cannot prefer something that did not exist when they were trained. Freshness is a retrieval bias, not a mystical love of newness.

• “Write in an AI‑friendly style with super‑clear headers and you’ll get cited.” LLMs are not grading your blog format. They see text, not your pretty design system. Clear structure helps search engines and humans, but it is not a magic LLM‑ranking switch.

• “Optimize your content for one tool (Perplexity / ChatGPT) and it will generalize.” Each product has different integrations, update cycles, and guardrails. You can track patterns, but there is no universal “LLM SEO checklist” that unlocks all of them at once.

These myths are attractive because they turn an uncomfortable truth (“you are not visible enough across the web”) into a comforting tactic (“just rewrite everything and add more FAQs”). But you cannot AB‑test your way out of a visibility gap if the retrieval layer never sees you.

So what actually works?

If you strip away the hype, LLM “optimization” looks very familiar:

• Win more surface area in search. Not just trophy terms, but the long‑tail, the comparisons, the “X vs Y”, the “best for Z”, the “how do I” questions that show up in fan‑outs.

• Build real entity presence. Make your brand, people, and products show up across different sites and formats. Mentions matter, not just links.

• Create content that is easy to quote. Clear, declarative statements, strong definitions, obvious “this is the answer” sections. Humans like them, and LLMs reach for them when they need to sound confident.

• Attach yourself to the sources LLMs love. That means being present on platforms that already dominate SERPs: Reddit, YouTube, developer docs, high‑authority blogs, niche communities. Not with spam, but with genuine, high‑signal contributions.

The uncomfortable part is that this is slower and harder than tweaking metadata. The good news is that if you are already good at SEO and content, you are not learning an entirely new discipline. You are just playing on a slightly different board.

The mindset shift SEOs need

Stop asking “how do I rank in LLMs?” and start asking:

• “Where does the retrieval stack get its evidence when people ask real questions about my space?”

• “When query fan‑out explodes my core keyword into a dozen variants, am I still there, or do I vanish?”

• “If an LLM had to explain my product to a stranger, what pieces of content across the web would it lean on—and do I own any of them?”

The post What is LLM Visibility? appeared first on Primary Position SEO NYC.

From Primary Position Research – the most comprehensive list of AI SEO/GEO tools on the market today

The GEO /AEO Visibility Tracking Tools Directory 

The post The Best, Most Comprehensive List of AEO/GEO Tracking Tools available in 2026 appeared first on Primary Position SEO NYC.

LLMs do not use E‑E‑A‑T, and treating E‑E‑A‑T as a ranking factor for them completely misunderstands how these systems work.

E‑E‑A‑T is a human narrative, not a machine signal

E‑E‑A‑T (Experience, Expertise, Authoritativeness, Trustworthiness) was invented as a framework for human raters and content marketers, not as a concrete, machine‑readable ranking signal inside an LLM.
It is a story layer we tell ourselves about “quality,” but a model does not see “author bio”, “brand”, or “trust badges” as first‑class, explicit signals the way SEOs talk about them.

LLMs operate on token sequences and learned weights. They see probabilities over words and patterns, not a checklist that says “this page has experience” or “this author is authoritative.”
When people say “LLMs reward E‑E‑A‑T,” they’re retrofitting a human QA framework onto a statistical language model that has no idea those letters even exist.

LLMs are not search engines

E‑E‑A‑T was always framed in the context of search: crawlers, indexes, ranking systems, and evaluators judging page quality. LLMs, by contrast, are generative models. They:

• Do not crawl the web in real time.

• Do not maintain a link graph and run a ranking algorithm like PageRank.

• Do not “score” a URL and store an E‑E‑A‑T value for it.

They generate the next token based on the distribution learned during training. Any retrieval layer on top (RAG, GEO, QFO, etc.) uses an external search/index system to fetch documents, but the model itself is not doing what Google Search does.
If the underlying engine is not a search engine, importing a search‑quality framework like E‑E‑A‑T into it is a category error.

What LLMs actually optimize for

During pre‑training, LLMs are optimized to reduce prediction error: given a sequence of tokens, predict the most likely next token. That is all. There is no slot in that objective for “expertise” or “author name”, only patterns that correlate with them in text.
If pages that read like expert content are common and consistent in the training data, the model will mimic that style and structure – not because it values E‑E‑A‑T, but because those patterns reduce loss.

During alignment (RLHF, RLAIF), models are nudged toward being “helpful,” “honest,” and “harmless,” but again this is framed as reward signals over outputs, not as “go find the most authoritative cardiologist’s blog.”
The model learns that answers that sound more cautious, cite sources, or hedge around medical and legal advice get rewarded. That is not E‑E‑A‑T, it is pattern‑reinforced style.

Retrieval is not E‑E‑A‑T scoring

When an LLM interface does retrieval (Perplexity, Claude, ChatGPT browsing, Gemini with search), that retrieval layer usually relies on:

• A traditional search index (Google/Bing or BraveSearch for example).

• A vector index over content chunks.

Those systems may have their own scoring functions (BM25, PageRank, embedding similarity, freshness, simple site‑level heuristics), but none of that is “E‑E‑A‑T” in the SEO‑blog sense.
The retriever is trying to surface documents that look textually relevant and sometimes fresh; the LLM is then summarizing or synthesizing those snippets. At no point does it need a concept like “this author has 20 years’ experience, give them +10% rank.”

E‑E‑A‑T is a narrative we lay over a complex set of retrieval, ranking, and quality systems in Google. Importing that same narrative into LLM retrieval is projection.

Why “E‑E‑A‑T SEO for LLMs” is marketing fiction

A lot of content around “how to build E‑E‑A‑T for AI Overviews / LLMs” does the same thing over and over: take a fuzzy idea and expand it into a full mythos because it’s easy to sell.
Instead of saying “LLMs don’t care about your author bios; they care if your content is in the index they use and matches the decomposed queries,” people bolt E‑E‑A‑T onto everything because it feels like a unifying theory.

The reality:

• LLMs need inputs that are retrievable via query fan‑out and embedding similarity, not pages blessed with E‑E‑A‑T.

• They sample passages that best match the decomposed intents; they do not check whether your “About” page proves you are a doctor.

• They hallucinate confidently regardless of whether the underlying source is “authoritative” by human standards. That alone should destroy the myth that E‑E‑A‑T is in play.

“E‑E‑A‑T for LLMs” mostly translates into: write clear, specific, well‑scoped content that fits the fan‑out queries and is easy to chunk and retrieve. That is about structure and coverage, not about brand reputation signals.

The real levers for LLM visibility

LLM layers rely heavily on existing search infrastructure for discovery and retrieval, so traditional crawlability, internal linking, and basic SEO hygiene still matter. None of these require belief in E‑E‑A‑T as a thing LLMs “use.” They require understanding how queries get expanded, how chunks get embedded, and how retrieval pipelines feed the model. The work now is not “building E‑E‑A‑T for LLMs.” It is understanding how your content gets pulled into query fan‑out pipelines and making sure it is the cleanest, clearest, most directly useful input those systems can find.

The post Understanding LLMs vs EEAT in SEO and GEO appeared first on Primary Position SEO NYC.

Why plugin “SEO scores” are nonsense

Most plugin or SaaS “SEO scores” are just glorified checklists:

• They test whether your keyword is in the slug, title, H1, intro, headings, body, alt text, and whether you wrote enough words.

• They might add a bit of sentiment (“positive term in title”) and then pretend this 100‑point system predicts rankings.

That kind of scorecard only measures how loudly you’re screaming “this page is about X,” not whether you can actually rank for X. It ignores two realities: Google cares about relevance and authority, and you cannot brute‑force your way into a competitive SERP with perfect on‑page rituals on a weak site.

The only scorecard that matters

A Weblinkr‑style scorecard has two main columns: Relevance and Authority.

Relevance (per page / query group)

• Query and intent defined: exactly which search and what user job this page is for.

• On‑page focus: URL, title, H1, and intro clearly aligned to that query, not 5 queries at once.

• Internal context: logical internal links in and out, with anchor text that reinforces the topic.

• SERP fit: your format and depth actually match what’s currently ranking (guides vs tools vs product pages).

If you can’t write a one‑line description of “this page wins for [query] because…”, your relevance score is low no matter what any plugin says.

Authority (site and page level)

• Domain‑level strength: real links from real sites in your niche, not junk metrics.

• Page‑level links: any actual links to this specific URL or closely related URLs.

• Brand footprint: mentions, reviews, or community presence that show you exist outside your own site.

• Behavioral proof: the page retains traffic and earns links over time instead of bouncing and dying.

On a real scorecard, a page with weak authority gets a low “can realistically rank” flag, even if every on‑page box is checked.

How to build a practical scorecard

Set this up in a spreadsheet or Notion, not in a plugin. For each important page, give simple scores like 0/1/2 (bad/ok/strong) instead of pretend‑precise numbers.

Columns to include:

• Target query & intent

• URL / title / H1 alignment

• Internal links in (count + quality)

• SERP match (format, depth, angle)

• Page‑level links

• Domain authority in this topic (low/medium/high, based on reality, not DA)

• Business priority (low/medium/high)

Your “SEO grade” for a page is not a single number; it’s the combination of can we be relevant here? and do we have enough authority to matter? Every strategy decision flows from those two answers.

How to use the scorecard with clients or bosses

The scorecard is not there to impress anyone with 95/100; it’s there to make trade‑offs obvious:

• “We are fully relevant for this query but have zero authority, so no, your rankings won’t move without links.”

• “We have plenty of authority but no focused page for this cluster, so we need to build one and wire it in internally.”

• “This keyword is mathematically unwinnable right now; we should prioritize easier topics where our authority is enough.”

Pair the scorecard with a plain‑language summary: 3–5 bullets on where you are blocked by relevance, where you are blocked by authority, and what you will do about each. That’s the only SEO “report card” that’s actually worth anything.

The post A perfect SEO scorecard perhaps? appeared first on Primary Position SEO NYC.

E‑E‑A‑T is not something you “add” to a page. It’s a reviewer concept and a sanity check Google uses so humans can talk about “is this real, is this safe, is this useful?” If you’re trying to optimize for EEAT as if it’s a ranking factor, you’re already in the wrong frame.

Stop chasing “EEAT signals”

Here’s the first thing you need to accept: there is no EEAT scorecard and there are no EEAT tags or technical signals you can sprinkle around to make rankings go up.

• You can’t “enable EEAT” in your CMS.

• You can’t buy a tool that measures it accurately.

• You definitely can’t fix a weak site by dropping the acronym in your copy.

Most “EEAT optimization” services are just rebranded content or CRO work wrapped in buzzwords to sound more Google‑official than they really are.

EEAT is about impression, not props

Google uses E‑E‑A‑T as a way for humans (quality raters, policy reviewers, abuse teams) to express how a site feels:

• Does this look like a real business or a fake one?

• Is this content written from real experience or scraped/fluffed?

• Would a normal person trust this source on health/money/legal topics?

That impression comes from the totality of your presence, not from a single trick:

• Real entity: clear who runs the site, with consistent names, addresses, and profiles.

• Real footprint: people talk about you off your own domain—links, mentions, citations.

• Real depth: content that shows you’ve actually done the thing, not just summarized the first page of Google.

You can’t “declare” any of that into existence with a byline and a stock headshot.

How to ignore bad EEAT advice

When you see an “EEAT guide,” run it through this filter:

• If it sounds like a checklist (“add author schema, add About page, add FAQs”) and ignores links, reputation, or actual expertise, treat it as decoration advice.

• If it claims “Google measures EEAT signals” but never explains real, verifiable mechanisms, it’s storytelling for sales, not reality.

• If it sells an “EEAT audit” that looks like a content rewrite with some boilerplate bios, you’re paying for lipstick on a pig.

Use EEAT as a mental model: “would a human with the power to nuke spam or restore false positives look at this site and consider it real, competent, and safe?” If the answer is yes, congratulations—you don’t have an EEAT problem; you have a classic SEO problem: crawl, index, competition, links, intent, and content quality.

Spend your time fixing those. The more your site behaves like an actually trustworthy entity in the real world, the less you ever need to say the word “EEAT” at all.

The post Guide to Google EEAT Audits appeared first on Primary Position SEO NYC.

A perennial conundrum echoes across the digital amphitheaters of SEO discourse—from the sprawling threads of Reddit to the focused exchanges of professional forums: How, precisely, does one fabricate or astro-turf the metric known as Domain Authority (DA)?

The Primitive Assumption: Backlinks as the Sole Determinant

The initial, and perhaps most instinctive, hypothesis posits that DA is fundamentally a function of Backlinks. If this were universally true, the manipulation of this score would be a relatively straightforward exercise of data obfuscation—a simple “twiddling” of the inputs fed into the analytical tools.

Consider a practical thought experiment: I engineered a scenario wherein I induced SEMRush to believe my organic Google traffic had surged by 50% within a single, arbitrary reporting period (a day or a week). This was achieved not through direct backlink manipulation, but by the injection of six AI-generated blog posts—a rapid content deployment that typically requires 1-2 weeks for keyword ranking positions outside of standard SERP reports to normalize.

The Refutation: DA Transcends Simple Link Equity

My subsequent empirical investigation, conducted on my own established domain, was designed to disprove the primacy of backlinks in the DA equation. The results, evidenced by the forthcoming screenshots (omitted here for brevity), suggest a more nuanced reality: DA is not solely predicated on backlink volume or quality.

These proprietary metrics are, in fact, demonstrably tweakable via estimated Organic Traffic. The rationale is inherently recursive: a tool expects pages with higher calculated DA to exhibit superior ranking performance; therefore, estimated performance acts as an input to the score itself.

The Empirical Setup: A Serendipitous Detachment

The experiment’s genesis lay in a moment of serendipitous external factor: I noticed that SEMRush inexplicably registered a sudden “loss” of approximately 2,000 backlinks. The origin and subsequent departure of these links remain an external variable, a loss over which I held neither control nor care.

However, I retained control over my internal content production. Operating strictly within my established topical authority (Google, SEO, etc.), I deployed eight blog posts in a single week. The result was a dramatic shift:

• SEMRush recalibrated its estimate of my monthly traffic, surging from 6,200 to 9,200 clicks in just one day.

• Crucially, the backlink graph concurrently showed that both total backlinks and referring domains dropped by almost 40%.

Simultaneously, while backlink metrics plummeted, my estimated traffic rose by 50%, and my Domain Authority increased from 26 to 30.

The Non-Linearity of Authority Scaling

This jump from DA 26 to 30 highlights a critical point: DA is a sliding, non-linear scale. The ascent from DA 29 to DA 30 requires a quantum leap in imputed authority that is disproportionately greater than the progress from, say, DA 15 to DA 21.

This tries to follow the foundational principles of PageRank , where the value of authority is diluted by the total number of links on a page. The lower a link appears on a page, the less “authority flow” it transmits. Similarly, increasing one’s DA at the higher end of the scale demands a massive, exponential input—an input that, in this case, appears to be heavily weighted toward demonstrated organic traffic and topical relevance, rather than simple link counting.

The completion of a 1,500-word treatise would further unpack how this content dilution and traffic estimation intersect to redefine the modern notion of “authority.”

Conclusion

SEMRush DA is very easy to fake or astro-turf. But so is SEO in general. If you really understand backlinks, PageRank and topical authority then you can move needles, including in Google.

The post How to fake Site Domain Authority or DA in SEO Tools like SEMRush, Ahrefs, Moz appeared first on Primary Position SEO NYC.

What is an exact match domain?

An exact match domain is a domain name that matches a target keyword or query phrase as closely as possible.
Examples:

• plumberlosangeles.com

• personalinjurylawyernyc.com

• bestcoffeesubscription.com

Instead of a purely branded name, the domain itself states the service and often the location. That simple alignment is the foundation of why EMDs can still matter in SEO.

Current SEO Strategic thinking

Exact match domains aren’t an old-school gimmick – they’re a structural advantage when you understand how search actually connects queries, brands, and domains. The conversations with Edward Sturm just make that more explicit and frame exact match domains

• Keywords tie brands to domains: Reddit comments spell out that one of the core ways Google learns that a brand and a domain belong together is that the keyword (brand or non-brand) appears in both. That same mechanism helps EMDs: a domain containing the query is inherently easier for the system to associate with that query.​

• While EMDs are not a button, they are more than an edge: In Edward Sturm’s content and David’s podcast segments, EMDs are presented as an edge in the mix—something like “microsoft-inc.com” having a better shot than “tips-inc.com” as long as it carries authority, not an auto-win with no links or content.​​

• Using keyword-in-domain to rank and re-rank content fast: shows cases where pages jump from invisible to top rankings when republished on better-aligned URLs and domains for the underlying trick. The principle is simple: when URL and domain semantics match the target query more tightly, the document is easier to classify and trust.​​

• “Go buy the keyword in the domain” as a modern playbook: In one of a long-form interview with Edward Sturm on YouTube, David talks about loving the “old” EMD playbook in an LLM world: just buy the keyword in the domain and build a real site on it, like the freeloadbalancer.com example he mentions. It is essentially treating the domain as your strongest on-page signal.​

Why this makes EMDs feel “critical” in their model

EMDs are critical because they:

• Compress the time-to-relevance: A new or weak site with an exact or near-exact match domain can punch above its weight against more authoritative but semantically vague brands, especially in local and niche markets.​

• Simplify topical authority: When the domain is the topic, every supporting page, internal link, and anchor text naturally reinforces a single concept—making topical authority easier to build and maintain.

• Make links and anchors easier: People naturally link using your domain name; if the domain name contains the keyword, you get keyword-rich anchors “for free” without asking anyone to optimize their link text​

How to apply their EMD philosophy without getting burned

Taking their claims seriously does not mean buying hundreds of spammy domains; it means using EMDs as a precision tool:

• Deploy EMDs where intent is tight and commercial (local services, specific B2B offers, high-intent affiliate terms), not as a replacement for every brand site.

• Build real properties on EMDs—solid content, UX, and links—so the domain advantage amplifies a strong site instead of making a weak site more obviously low quality.

• Think portfolio: a branded hub + carefully chosen EMDs for critical revenue terms, stitched together with coherent strategy, not churn-and-burn microsites.

The post Why I believe Exact Match Domains (EMDs) are back in SEO appeared first on Primary Position SEO NYC.